A SELF-SYSTEMATIZE DEPENDENCE SCULPT FOR DECENTRALIZED SYSTEMS

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationship among the peers can mitigate attacks of malicious peers. This paper proposed the distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on relevant, recentness, and peer satisfaction parameters. Additionally, recommender’s trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on different malicious behavior models. In these experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.